{"id":13398935,"url":"https://github.com/NiuTrans/ABigSurvey","last_synced_at":"2025-03-14T03:30:55.483Z","repository":{"id":41561018,"uuid":"280339645","full_name":"NiuTrans/ABigSurvey","owner":"NiuTrans","description":"A collection of 1000+ survey papers on Natural Language Processing (NLP) and Machine Learning (ML).","archived":false,"fork":false,"pushed_at":"2024-03-31T12:43:28.000Z","size":1828,"stargazers_count":1961,"open_issues_count":4,"forks_count":236,"subscribers_count":110,"default_branch":"master","last_synced_at":"2024-06-21T17:07:50.614Z","etag":null,"topics":["deep-learning","machine-learning","natural-language-processing","neural-networks","paper-list","surveys"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/NiuTrans.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null}},"created_at":"2020-07-17T05:57:36.000Z","updated_at":"2024-06-18T15:57:54.000Z","dependencies_parsed_at":"2022-07-07T22:15:09.397Z","dependency_job_id":"3d9c33cf-b34b-4510-a343-cbd98595084f","html_url":"https://github.com/NiuTrans/ABigSurvey","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/NiuTrans%2FABigSurvey","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/NiuTrans%2FABigSurvey/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/NiuTrans%2FABigSurvey/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/NiuTrans%2FABigSurvey/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/NiuTrans","download_url":"https://codeload.github.com/NiuTrans/ABigSurvey/tar.gz/refs/heads/master","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":213240389,"owners_count":15557470,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["deep-learning","machine-learning","natural-language-processing","neural-networks","paper-list","surveys"],"created_at":"2024-07-30T19:00:32.828Z","updated_at":"2024-07-30T19:03:27.437Z","avatar_url":"https://github.com/NiuTrans.png","language":null,"funding_links":[],"categories":["MACHINE LEARNING","Topics","Others"],"sub_categories":["Unsupervised Learning"],"readme":" # A Survey of Surveys (NLP \u0026 ML)\n\nIn this document, we survey hundreds of survey papers on Natural Language  Processing (NLP) and Machine Learning (ML). We categorize these papers into popular topics and do simple counting for some interesting problems. In addition, we show the list of the papers with urls (1063 papers). \n\n:new: A list of LLM surveys is released! [Link](https://github.com/NiuTrans/ABigSurveyOfLLMs)\n\n## Categorization\n\nWe follow the ACL and ICML submission guideline of recent years, covering a broad range of areas in NLP and ML. The categorization is as follows:\n+ Natural Language Processing\n    + \u003ca href=\"#computational-social-science-and-social-media\"\u003eComputational Social Science and Social Media\u003c/a\u003e\n    + \u003ca href=\"#dialogue-and-interactive-systems\"\u003eDialogue and Interactive Systems\u003c/a\u003e\n    + \u003ca href=\"#generation\"\u003eGeneration\u003c/a\u003e\n    + \u003ca href=\"#information-extraction\"\u003eInformation Extraction\u003c/a\u003e\n    + \u003ca href=\"#information-retrieval-and-text-mining\"\u003eInformation Retrieval and Text Mining\u003c/a\u003e\n    + \u003ca href=\"#interpretability-and-analysis-of-models-for-nLP\"\u003eInterpretability and Analysis of Models for NLP\u003c/a\u003e\n    + \u003ca href=\"#knowledge-graph\"\u003eKnowledge Graph\u003c/a\u003e\n    + \u003ca href=\"#language-grounding-to-vision-robotics-and-beyond\"\u003eLanguage Grounding to Vision, Robotics and Beyond\u003c/a\u003e\n    + \u003ca href=\"#large-language-models\"\u003eLarge Language Models\u003c/a\u003e\n    + \u003ca href=\"#linguistic-theories-cognitive-modeling-and-psycholinguistics\"\u003eLinguistic Theories, Cognitive Modeling and Psycholinguistics\u003c/a\u003e\n    + \u003ca href=\"#machine-learning-for-nlp\"\u003eMachine Learning for NLP\u003c/a\u003e\n    + \u003ca href=\"#machine-translation\"\u003eMachine Translation\u003c/a\u003e\n    + \u003ca href=\"#named-entity-recognition\"\u003eNamed Entity Recognition\u003c/a\u003e\n    + \u003ca href=\"#natural-language-inference\"\u003eNatural Language Inference\u003c/a\u003e\n    + \u003ca href=\"#natural-language-processing\"\u003eNatural Language Processing\u003c/a\u003e\n    + \u003ca href=\"#nlp-applications\"\u003eNLP Applications\u003c/a\u003e\n    + \u003ca href=\"#pre-trained-models\"\u003ePre-trained Models\u003c/a\u003e\n    + \u003ca href=\"#prompt\"\u003ePrompt\u003c/a\u003e\n    + \u003ca href=\"#question-answering\"\u003eQuestion Answering\u003c/a\u003e\n    + \u003ca href=\"#reading-comprehension\"\u003eReading Comprehension\u003c/a\u003e\n    + \u003ca href=\"#recommender-systems\"\u003eRecommender Systems\u003c/a\u003e\n    + \u003ca href=\"#resources-and-evaluation\"\u003eResources and Evaluation\u003c/a\u003e\n    + \u003ca href=\"#semantics\"\u003eSemantics\u003c/a\u003e\n    + \u003ca href=\"#sentiment-analysis-stylistic-analysis-and-argument-mining\"\u003eSentiment Analysis, Stylistic Analysis and Argument Mining\u003c/a\u003e\n    + \u003ca href=\"#speech-and-multimodality\"\u003eSpeech and Multimodality\u003c/a\u003e\n    + \u003ca href=\"#summarization\"\u003eSummarization\u003c/a\u003e\n    + \u003ca href=\"#tagging-chunking-syntax-and-parsing\"\u003eTagging, Chunking, Syntax and Parsing\u003c/a\u003e\n    + \u003ca href=\"#text-classification\"\u003eText Classification\u003c/a\u003e\n+ Machine Learning\n    + \u003ca href=\"#architectures\"\u003eArchitectures\u003c/a\u003e\n    + \u003ca href=\"#automl\"\u003eAutoML\u003c/a\u003e\n    + \u003ca href=\"#bayesian-methods\"\u003eBayesian Methods\u003c/a\u003e\n    + \u003ca href=\"#classification-clustering-and-regression\"\u003eClassification, Clustering and Regression\u003c/a\u003e\n    + \u003ca href=\"#computer-vision\"\u003eComputer Vision\u003c/a\u003e\n    + \u003ca href=\"#contrastive-learning\"\u003eContrastive Learning\u003c/a\u003e\n    + \u003ca href=\"#curriculum-learning\"\u003eCurriculum Learning\u003c/a\u003e\n    + \u003ca href=\"#data-augmentation\"\u003eData Augmentation\u003c/a\u003e\n    + \u003ca href=\"#deep-learning-general-methods\"\u003eDeep Learning General Methods\u003c/a\u003e\n    + \u003ca href=\"#deep-reinforcement-learning\"\u003eDeep Reinforcement Learning\u003c/a\u003e\n    + \u003ca href=\"#diffusion-models\"\u003eDiffusion Models\u003c/a\u003e\n    + \u003ca href=\"#federated-learning\"\u003eFederated Learning\u003c/a\u003e\n    + \u003ca href=\"#few-shot-and-zero-shot-learning\"\u003eFew-Shot and Zero-Shot Learning\u003c/a\u003e\n    + \u003ca href=\"#general-machine-learning\"\u003eGeneral Machine Learning\u003c/a\u003e\n    + \u003ca href=\"#generative-adversarial-networks\"\u003eGenerative Adversarial Networks\u003c/a\u003e\n    + \u003ca href=\"#graph-neural-networks\"\u003eGraph Neural Networks\u003c/a\u003e\n    + \u003ca href=\"#interpretability-and-analysis\"\u003eInterpretability and Analysis\u003c/a\u003e\n    + \u003ca href=\"#knowledge-distillation\"\u003eKnowledge Distillation\u003c/a\u003e\n    + \u003ca href=\"#meta-learning\"\u003eMeta Learning\u003c/a\u003e\n    + \u003ca href=\"#metric-learning\"\u003eMetric Learning\u003c/a\u003e\n    + \u003ca href=\"#ml-and-dl-applications\"\u003eML and DL Applications\u003c/a\u003e\n    + \u003ca href=\"#model-compression-and-acceleration\"\u003eModel Compression and Acceleration\u003c/a\u003e\n    + \u003ca href=\"#multi-label-learning\"\u003eMulti-Label Learning\u003c/a\u003e\n    + \u003ca href=\"#multi-task-and-multi-view-learning\"\u003eMulti-Task and Multi-View Learning\u003c/a\u003e\n    + \u003ca href=\"#online-learning\"\u003eOnline Learning\u003c/a\u003e\n    + \u003ca href=\"#optimization\"\u003eOptimization\u003c/a\u003e\n    + \u003ca href=\"#semi-supervised-weakly-supervised-and-unsupervised-learning\"\u003eSemi-Supervised,-Weakly-Supervised-and-Unsupervised-Learning\u003c/a\u003e\n    + \u003ca href=\"#transfer-learning\"\u003eTransfer Learning\u003c/a\u003e\n    + \u003ca href=\"#trustworthy-machine-learning\"\u003eTrustworthy Machine Learning\u003c/a\u003e\n\n\nTo reduce class imbalance, we separate some of the hot sub-topics from the original categorization of ACL and ICML submissions. E.g., Named Entity Recognition is a first-level area in our categorization because it is the focus of several surveys.\n\n## Statistics\n\nWe show the number of paper in each area in Figures 1-2.\n\n\u003cp align=\"center\"\u003e\u003cimg src=\"https://s2.loli.net/2023/05/26/DUa43miWf5NFlZx.png\" width=\"70%\" height=\"70%\"/\u003e\u003c/p\u003e\n\n\u003cp align=\"center\"\u003eFigure 1: # of papers in each NLP area.\u003c/p\u003e\n\n\u003cp align=\"center\"\u003e\u003cimg src=\"https://s2.loli.net/2023/05/26/z3PslUXbZFd6qrB.png\" width=\"70%\" height=\"70%\"/\u003e\u003c/p\u003e\n\n\u003cp align=\"center\"\u003eFigure 2:  # of papers in each ML area.\u003c/p\u003e\n\nAlso, we plot paper number as a function of publication year (see Figure 3).\n\n\u003cp align=\"center\"\u003e\u003cimg src=\"https://s2.loli.net/2023/05/26/7tMmcRO1lK9N5hF.png\" width=\"70%\" height=\"70%\"/\u003e\u003c/p\u003e\n\n\u003cp align=\"center\"\u003eFigure 3: # of papers vs publication year.\u003c/p\u003e\n\nIn addition, we generate word clouds to show hot topics in these surveys (see Figures 4-5).\n\n\u003cp align=\"center\"\u003e\u003cimg src=\"https://s2.loli.net/2023/05/26/6RqNCKBwsEZtA3H.png\" width=\"60%\" height=\"60%\"/\u003e\u003c/p\u003e\n\n\u003cp align=\"center\"\u003eFigure 4: The word cloud for NLP.\u003c/p\u003e\n\n\u003cp align=\"center\"\u003e\u003cimg src=\"https://s2.loli.net/2023/05/26/zln92QYvmGLWMUE.png\" width=\"60%\" height=\"60%\"/\u003e\u003c/p\u003e\n\n\u003cp align=\"center\"\u003eFigure 5: The word cloud for ML.\u003c/p\u003e\n\n\n## The NLP Paper List\n\n#### [Computational Social Science and Social Media](#content)\n\n1. **A Comprehensive Survey on Community Detection with Deep Learning.** arXiv 2021 [paper](https://arxiv.org/pdf/2105.12584.pdf) [bib](/bib/Natural-Language-Processing/Computational-Social-Science-and-Social-Media/Su2021A.md)\n\n    *Xing Su, Shan Xue, Fanzhen Liu, Jia Wu, Jian Yang, Chuan Zhou, Wenbin Hu, Cécile Paris, Surya Nepal, Di Jin, Quan Z. Sheng, Philip S. Yu*\n\n2. **A Survey of Fake News: Fundamental Theories, Detection Methods, and Opportunities.** ACM Comput. Surv. 2021 [paper](https://arxiv.org/abs/1812.00315) [bib](/bib/Natural-Language-Processing/Computational-Social-Science-and-Social-Media/Zhou2021A.md)\n\n    *Xinyi Zhou, Reza Zafarani*\n\n3. **A Survey of Race, Racism, and Anti-Racism in NLP.** ACL 2021 [paper](https://arxiv.org/abs/2106.11410) [bib](/bib/Natural-Language-Processing/Computational-Social-Science-and-Social-Media/Field2021A.md)\n\n    *Anjalie Field, Su Lin Blodgett, Zeerak Waseem, Yulia Tsvetkov*\n\n4. **A Survey on Computational Propaganda Detection.** IJCAI 2020 [paper](https://arxiv.org/pdf/2007.08024.pdf) [bib](/bib/Natural-Language-Processing/Computational-Social-Science-and-Social-Media/Martino2020A.md)\n\n    *Giovanni Da San Martino, Stefano Cresci, Alberto Barrón-Cedeño, Seunghak Yu, Roberto Di Pietro, Preslav Nakov*\n\n5. **A Survey on Trust Prediction in Online Social Networks.** IEEE Access 2020 [paper](https://ieeexplore.ieee.org/abstract/document/9142365) [bib](/bib/Natural-Language-Processing/Computational-Social-Science-and-Social-Media/Ghafari2020A.md)\n\n    *Seyed Mohssen Ghafari, Amin Beheshti, Aditya Joshi, Cécile Paris, Adnan Mahmood, Shahpar Yakhchi, Mehmet A. Orgun*\n\n6. **Computational Sociolinguistics: A Survey.** Comput. Linguistics 2016 [paper](https://arxiv.org/abs/1508.07544) [bib](/bib/Natural-Language-Processing/Computational-Social-Science-and-Social-Media/Nguyen2016Computational.md)\n\n    *Dong Nguyen, A. Seza Dogruöz, Carolyn P. Rosé, Franciska de Jong*\n\n7. **Confronting Abusive Language Online: A Survey from the Ethical and Human Rights Perspective.** J. Artif. Intell. Res. 2021 [paper](https://arxiv.org/abs/2012.12305) [bib](/bib/Natural-Language-Processing/Computational-Social-Science-and-Social-Media/Kiritchenko2021Confronting.md)\n\n    *Svetlana Kiritchenko, Isar Nejadgholi, Kathleen C. Fraser*\n\n8. **From Symbols to Embeddings: A Tale of Two Representations in Computational Social Science.** J. Soc. Comput. 2021 [paper](https://arxiv.org/pdf/2106.14198) [bib](/bib/Natural-Language-Processing/Computational-Social-Science-and-Social-Media/Chen2021From.md)\n\n    *Huimin Chen, Cheng Yang, Xuanming Zhang, Zhiyuan Liu, Maosong Sun, Jianbin Jin*\n\n9. **Language (Technology) is Power: A Critical Survey of \"Bias\" in NLP.** ACL 2020 [paper](https://arxiv.org/abs/2005.14050) [bib](/bib/Natural-Language-Processing/Computational-Social-Science-and-Social-Media/Blodgett2020Language.md)\n\n    *Su Lin Blodgett, Solon Barocas, Hal Daumé III, Hanna M. Wallach*\n\n10. **Societal Biases in Language Generation: Progress and Challenges.** ACL 2021 [paper](https://arxiv.org/pdf/2105.04054.pdf) [bib](/bib/Natural-Language-Processing/Computational-Social-Science-and-Social-Media/Sheng2021Societal.md)\n\n    *Emily Sheng, Kai-Wei Chang, Prem Natarajan, Nanyun Peng*\n\n11. **Tackling Online Abuse: A Survey of Automated Abuse Detection Methods.** arXiv 2019 [paper](https://arxiv.org/pdf/1908.06024.pdf) [bib](/bib/Natural-Language-Processing/Computational-Social-Science-and-Social-Media/Mishra2019Tackling.md)\n\n    *Pushkar Mishra, Helen Yannakoudakis, Ekaterina Shutova*\n\n12. **When do Word Embeddings Accurately Reflect Surveys on our Beliefs About People?.** ACL 2020 [paper](https://arxiv.org/abs/2004.12043) [bib](/bib/Natural-Language-Processing/Computational-Social-Science-and-Social-Media/Joseph2020When.md)\n\n    *Kenneth Joseph, Jonathan H. Morgan*\n\n#### [Dialogue and Interactive Systems](#content)\n\n1. **A Survey of Arabic Dialogues Understanding for Spontaneous Dialogues and Instant Message.** arXiv 2015 [paper](https://arxiv.org/abs/1505.03084) [bib](/bib/Natural-Language-Processing/Dialogue-and-Interactive-Systems/Elmadany2015A.md)\n\n    *AbdelRahim A. Elmadany, Sherif M. Abdou, Mervat Gheith*\n\n2. **A Survey of Available Corpora For Building Data-Driven Dialogue Systems: The Journal Version.** Dialogue Discourse 2018 [paper](https://journals.uic.edu/ojs/index.php/dad/article/view/10733/9501) [bib](/bib/Natural-Language-Processing/Dialogue-and-Interactive-Systems/Serban2018A.md)\n\n    *Iulian Vlad Serban, Ryan Lowe, Peter Henderson, Laurent Charlin, Joelle Pineau*\n\n3. **A Survey of Document Grounded Dialogue Systems (DGDS).** arXiv 2020 [paper](https://arxiv.org/abs/2004.13818) [bib](/bib/Natural-Language-Processing/Dialogue-and-Interactive-Systems/Ma2020A.md)\n\n    *Longxuan Ma, Wei-Nan Zhang, Mingda Li, Ting Liu*\n\n4. **A Survey of Intent Classification and Slot-Filling Datasets for Task-Oriented Dialog.** arXiv 2022 [paper](https://arxiv.org/pdf/2207.13211.pdf) [bib](/bib/Natural-Language-Processing/Dialogue-and-Interactive-Systems/Larson2022A.md)\n\n    *Stefan Larson, Kevin Leach*\n\n5. **A Survey of Natural Language Generation Techniques with a Focus on Dialogue Systems - Past, Present and Future Directions.** arXiv 2019 [paper](https://arxiv.org/abs/1906.00500) [bib](/bib/Natural-Language-Processing/Dialogue-and-Interactive-Systems/Santhanam2019A.md)\n\n    *Sashank Santhanam, Samira Shaikh*\n\n6. **A survey of neural models for the automatic analysis of conversation: Towards a better integration of the social sciences.** arXiv 2022 [paper](https://arxiv.org/pdf/2203.16891.pdf) [bib](/bib/Natural-Language-Processing/Dialogue-and-Interactive-Systems/Clavel2022A.md)\n\n    *Chloé Clavel, Matthieu Labeau, Justine Cassell*\n\n7. **A Survey on Dialog Management: Recent Advances and Challenges.** arXiv 2020 [paper](https://arxiv.org/abs/2005.02233) [bib](/bib/Natural-Language-Processing/Dialogue-and-Interactive-Systems/Dai2020A.md)\n\n    *Yinpei Dai, Huihua Yu, Yixuan Jiang, Chengguang Tang, Yongbin Li, Jian Sun*\n\n8. **A Survey on Dialogue Systems: Recent Advances and New Frontiers.** SIGKDD Explor. 2017 [paper](https://arxiv.org/abs/1711.01731) [bib](/bib/Natural-Language-Processing/Dialogue-and-Interactive-Systems/Chen2017A.md)\n\n    *Hongshen Chen, Xiaorui Liu, Dawei Yin, Jiliang Tang*\n\n9. **Advances in Multi-turn Dialogue Comprehension: A Survey.** arXiv 2021 [paper](https://arxiv.org/abs/2103.03125) [bib](/bib/Natural-Language-Processing/Dialogue-and-Interactive-Systems/Zhang2021Advances.md)\n\n    *Zhuosheng Zhang, Hai Zhao*\n\n10. **Challenges in Building Intelligent Open-domain Dialog Systems.** ACM Trans. Inf. Syst. 2020 [paper](https://arxiv.org/abs/1905.05709) [bib](/bib/Natural-Language-Processing/Dialogue-and-Interactive-Systems/Huang2020Challenges.md)\n\n    *Minlie Huang, Xiaoyan Zhu, Jianfeng Gao*\n\n11. **Conversational Agents: Theory and Applications.** arXiv 2022 [paper](https://arxiv.org/pdf/2202.03164.pdf) [bib](/bib/Natural-Language-Processing/Dialogue-and-Interactive-Systems/Wahde2022Conversational.md)\n\n    *Mattias Wahde, Marco Virgolin*\n\n12. **Conversational Machine Comprehension: a Literature Review.** COLING 2020 [paper](https://arxiv.org/abs/2006.00671) [bib](/bib/Natural-Language-Processing/Dialogue-and-Interactive-Systems/Gupta2020Conversational.md)\n\n    *Somil Gupta, Bhanu Pratap Singh Rawat, Hong Yu*\n\n13. **How to Evaluate Your Dialogue Models: A Review of Approaches.** arXiv 2021 [paper](https://arxiv.org/pdf/2108.01369.pdf) [bib](/bib/Natural-Language-Processing/Dialogue-and-Interactive-Systems/Li2021How.md)\n\n    *Xinmeng Li, Wansen Wu, Long Qin, Quanjun Yin*\n\n14. **Neural Approaches to Conversational AI.** ACL 2018 [paper](https://arxiv.org/pdf/1809.08267) [bib](/bib/Natural-Language-Processing/Dialogue-and-Interactive-Systems/Gao2018Neural.md)\n\n    *Jianfeng Gao, Michel Galley, Lihong Li*\n\n15. **Neural Approaches to Conversational AI: Question Answering, Task-oriented Dialogues and Social Chatbots.** Now Foundations and Trends 2019 [paper](https://ieeexplore.ieee.org/document/8649787) [bib](/bib/Natural-Language-Processing/Dialogue-and-Interactive-Systems/Gao2019Neural.md)\n\n    *Jianfeng Gao, Michel Galley, Lihong Li*\n\n16. **POMDP-Based Statistical Spoken Dialog Systems: A Review.** Proc. IEEE 2013 [paper](https://ieeexplore.ieee.org/document/6407655/) [bib](/bib/Natural-Language-Processing/Dialogue-and-Interactive-Systems/Young2013POMDP-Based.md)\n\n    *Steve J. Young, Milica Gasic, Blaise Thomson, Jason D. Williams*\n\n17. **Recent Advances and Challenges in Task-oriented Dialog System.** arXiv 2020 [paper](https://arxiv.org/pdf/2003.07490) [bib](/bib/Natural-Language-Processing/Dialogue-and-Interactive-Systems/Zhang2020Recent.md)\n\n    *Zheng Zhang, Ryuichi Takanobu, Minlie Huang, Xiaoyan Zhu*\n\n18. **Recent Advances in Deep Learning Based Dialogue Systems: A Systematic Survey.** arXiv 2021 [paper](https://arxiv.org/pdf/2105.04387.pdf) [bib](/bib/Natural-Language-Processing/Dialogue-and-Interactive-Systems/Ni2021Recent.md)\n\n    *Jinjie Ni, Tom Young, Vlad Pandelea, Fuzhao Xue, Vinay Adiga, Erik Cambria*\n\n19. **Utterance-level Dialogue Understanding: An Empirical Study.** arXiv 2020 [paper](https://arxiv.org/abs/2009.13902) [bib](/bib/Natural-Language-Processing/Dialogue-and-Interactive-Systems/Ghosal2020Utterance-level.md)\n\n    *Deepanway Ghosal, Navonil Majumder, Rada Mihalcea, Soujanya Poria*\n\n#### [Generation](#content)\n\n1. **A Survey of Controllable Text Generation using Transformer-based Pre-trained Language Models.** arXiv 2022 [paper](https://arxiv.org/pdf/2201.05337.pdf) [bib](/bib/Natural-Language-Processing/Generation/Zhang2022A.md)\n\n    *Hanqing Zhang, Haolin Song, Shaoyu Li, Ming Zhou, Dawei Song*\n\n2. **A Survey of Knowledge-Enhanced Text Generation.** ACM Comput. Surv. 2022 [paper](https://arxiv.org/pdf/2010.04389.pdf) [bib](/bib/Natural-Language-Processing/Generation/Yu2022A.md)\n\n    *Wenhao Yu, Chenguang Zhu, Zaitang Li, Zhiting Hu, Qingyun Wang, Heng Ji, Meng Jiang*\n\n3. **A Survey on Multi-hop Question Answering and Generation.** arXiv 2022 [paper](https://arxiv.org/pdf/2204.09140.pdf) [bib](/bib/Natural-Language-Processing/Generation/Mavi2022A.md)\n\n    *Vaibhav Mavi, Anubhav Jangra, Adam Jatowt*\n\n4. **A Survey on Retrieval-Augmented Text Generation.** arXiv 2022 [paper](https://arxiv.org/pdf/2202.01110.pdf) [bib](/bib/Natural-Language-Processing/Generation/Li2022A.md)\n\n    *Huayang Li, Yixuan Su, Deng Cai, Yan Wang, Lemao Liu*\n\n5. **A Survey on Text Simplification.** arXiv 2020 [paper](https://arxiv.org/abs/2008.08612) [bib](/bib/Natural-Language-Processing/Generation/Sikka2020A.md)\n\n    *Punardeep Sikka, Vijay Mago*\n\n6. **Automatic Detection of Machine Generated Text: A Critical Survey.** COLING 2020 [paper](https://arxiv.org/pdf/2011.01314.pdf) [bib](/bib/Natural-Language-Processing/Generation/Jawahar2020Automatic.md)\n\n    *Ganesh Jawahar, Muhammad Abdul-Mageed, Laks V. S. Lakshmanan*\n\n7. **Automatic Story Generation: Challenges and Attempts.** arXiv 2021 [paper](https://arxiv.org/abs/2102.12634) [bib](/bib/Natural-Language-Processing/Generation/Alabdulkarim2021Automatic.md)\n\n    *Amal Alabdulkarim, Siyan Li, Xiangyu Peng*\n\n8. **ChatGPT is not all you need. A State of the Art Review of large Generative AI models.** arXiv 2023 [paper](https://arxiv.org/pdf/2301.04655.pdf) [bib](/bib/Natural-Language-Processing/Generation/Gozalo-Brizuela2023ChatGPT.md)\n\n    *Roberto Gozalo-Brizuela, Eduardo C. Garrido-Merchán*\n\n9. **Content Selection in Data-to-Text Systems: A Survey.** arXiv 2016 [paper](https://arxiv.org/abs/1610.08375) [bib](/bib/Natural-Language-Processing/Generation/Gkatzia2016Content.md)\n\n    *Dimitra Gkatzia*\n\n10. **Data-Driven Sentence Simplification: Survey and Benchmark.** Comput. 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Lelkes, Vinh Q. Tran, Cong Yu*\n\n18. **Recent Advances in Neural Question Generation.** arXiv 2019 [paper](https://arxiv.org/abs/1905.08949) [bib](/bib/Natural-Language-Processing/Generation/Pan2019Recent.md)\n\n    *Liangming Pan, Wenqiang Lei, Tat-Seng Chua, Min-Yen Kan*\n\n19. **Recent Advances in SQL Query Generation: A Survey.** arXiv 2020 [paper](https://arxiv.org/abs/2005.07667) [bib](/bib/Natural-Language-Processing/Generation/Kalajdjieski2020Recent.md)\n\n    *Jovan Kalajdjieski, Martina Toshevska, Frosina Stojanovska*\n\n20. **Survey of Hallucination in Natural Language Generation.** arXiv 2022 [paper](https://arxiv.org/pdf/2202.03629.pdf) [bib](/bib/Natural-Language-Processing/Generation/Ji2022Survey.md)\n\n    *Ziwei Ji, Nayeon Lee, Rita Frieske, Tiezheng Yu, Dan Su, Yan Xu, Etsuko Ishii, Yejin Bang, Andrea Madotto, Pascale Fung*\n\n21. **Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation.** J. Artif. Intell. 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Support Syst. 2016 [paper](https://www.sciencedirect.com/science/article/abs/pii/S0167923616300173) [bib](/bib/Natural-Language-Processing/Information-Extraction/Hogenboom2016A.md)\n\n    *Frederik Hogenboom, Flavius Frasincar, Uzay Kaymak, Franciska de Jong, Emiel Caron*\n\n5. **A survey of joint intent detection and slot-filling models in natural language understanding.** arXiv 2021 [paper](https://arxiv.org/abs/2101.08091) [bib](/bib/Natural-Language-Processing/Information-Extraction/Weld2021A.md)\n\n    *Henry Weld, Xiaoqi Huang, Siqi Long, Josiah Poon, Soyeon Caren Han*\n\n6. **A Survey of Textual Event Extraction from Social Networks.** LPKM 2017 [paper](http://ceur-ws.org/Vol-1988/LPKM2017_paper_15.pdf) [bib](/bib/Natural-Language-Processing/Information-Extraction/Mejri2017A.md)\n\n    *Mohamed Mejri, Jalel Akaichi*\n\n7. **A Survey on Deep Learning Event Extraction: Approaches and Applications.** arXiv 2021 [paper](https://arxiv.org/pdf/2107.02126.pdf) [bib](/bib/Natural-Language-Processing/Information-Extraction/Li2021A.md)\n\n    *Qian Li, Jianxin Li, Jiawei Sheng, Shiyao Cui, Jia Wu, Yiming Hei, Hao Peng, Shu Guo, Lihong Wang, Amin Beheshti, Philip S. Yu*\n\n8. **A Survey on Open Information Extraction.** COLING 2018 [paper](https://arxiv.org/abs/1806.05599) [bib](/bib/Natural-Language-Processing/Information-Extraction/Niklaus2018A.md)\n\n    *Christina Niklaus, Matthias Cetto, André Freitas, Siegfried Handschuh*\n\n9. **A Survey on Temporal Reasoning for Temporal Information Extraction from Text (Extended Abstract).** IJCAI 2020 [paper](https://arxiv.org/abs/2005.06527) [bib](/bib/Natural-Language-Processing/Information-Extraction/Leeuwenberg2020A.md)\n\n    *Artuur Leeuwenberg, Marie-Francine Moens*\n\n10. **An Overview of Event Extraction from Text.** DeRiVE@ISWC 2011 [paper](http://ceur-ws.org/Vol-779/derive2011_submission_1.pdf) [bib](/bib/Natural-Language-Processing/Information-Extraction/Hogenboom2011An.md)\n\n    *Frederik Hogenboom, Flavius Frasincar, Uzay Kaymak, Franciska de Jong*\n\n11. **Automatic Extraction of Causal Relations from Natural Language Texts: A Comprehensive Survey.** arXiv 2016 [paper](https://arxiv.org/abs/1605.07895) [bib](/bib/Natural-Language-Processing/Information-Extraction/Asghar2016Automatic.md)\n\n    *Nabiha Asghar*\n\n12. **Complex Relation Extraction: Challenges and Opportunities.** arXiv 2020 [paper](https://arxiv.org/pdf/2012.04821.pdf) [bib](/bib/Natural-Language-Processing/Information-Extraction/Jiang2020Complex.md)\n\n    *Haiyun Jiang, Qiaoben Bao, Qiao Cheng, Deqing Yang, Li Wang, Yanghua Xiao*\n\n13. **Extracting Events and Their Relations from Texts: A Survey on Recent Research Progress and Challenges.** AI Open 2020 [paper](https://www.sciencedirect.com/science/article/pii/S266665102100005X/pdfft?md5=3983861e9ae91ce7b45f0c5533071077\u0026pid=1-s2.0-S266665102100005X-main.pdf) [bib](/bib/Natural-Language-Processing/Information-Extraction/Liu2020Extracting.md)\n\n    *Kang Liu, Yubo Chen, Jian Liu, Xinyu Zuo, Jun Zhao*\n\n14. **Knowledge Extraction in Low-Resource Scenarios: Survey and Perspective.** arXiv 2022 [paper](https://arxiv.org/pdf/2202.08063.pdf) [bib](/bib/Natural-Language-Processing/Information-Extraction/Deng2022Knowledge.md)\n\n    *Shumin Deng, Ningyu Zhang, Hui Chen, Feiyu Xiong, Jeff Z. 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Data Eng. 2022 [paper](https://arxiv.org/abs/1904.07695) [bib](/bib/Natural-Language-Processing/Information-Retrieval-and-Text-Mining/Qiang2022Short.md)\n\n    *Jipeng Qiang, Zhenyu Qian, Yun Li, Yunhao Yuan, Xindong Wu*\n\n12. **Taking Search to Task.** arXiv 2023 [paper](https://arxiv.org/pdf/2301.05046.pdf) [bib](/bib/Natural-Language-Processing/Information-Retrieval-and-Text-Mining/Shah2023Taking.md)\n\n    *Chirag Shah, Ryen W. White, Paul Thomas, Bhaskar Mitra, Shawon Sarkar, Nicholas J. Belkin*\n\n13. **Topic Modelling Meets Deep Neural Networks: A Survey.** IJCAI 2021 [paper](https://arxiv.org/abs/2103.00498) [bib](/bib/Natural-Language-Processing/Information-Retrieval-and-Text-Mining/Zhao2021Topic.md)\n\n    *He Zhao, Dinh Q. Phung, Viet Huynh, Yuan Jin, Lan Du, Wray L. Buntine*\n\n#### [Interpretability and Analysis of Models for NLP](#content)\n\n1. **A Primer in BERTology: What we know about how BERT works.** Trans. Assoc. Comput. 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Rashid, Anisa Rula, Lukas Schmelzeisen, Juan Sequeda, Steffen Staab, Antoine Zimmermann*\n\n13. **Knowledge Graphs: An Information Retrieval Perspective.** Found. Trends Inf. Retr. 2020 [paper](https://www.nowpublishers.com/article/Details/INR-063) [bib](/bib/Natural-Language-Processing/Knowledge-Graph/Reinanda2020Knowledge.md)\n\n    *Ridho Reinanda, Edgar Meij, Maarten de Rijke*\n\n14. **Multi-Modal Knowledge Graph Construction and Application: A Survey.** arXiv 2022 [paper](https://arxiv.org/pdf/2202.05786.pdf) [bib](/bib/Natural-Language-Processing/Knowledge-Graph/Zhu2022Multi-Modal.md)\n\n    *Xiangru Zhu, Zhixu Li, Xiaodan Wang, Xueyao Jiang, Penglei Sun, Xuwu Wang, Yanghua Xiao, Nicholas Jing Yuan*\n\n15. **Neural, Symbolic and Neural-symbolic Reasoning on Knowledge Graphs.** AI Open 2021 [paper](https://www.sciencedirect.com/science/article/pii/S2666651021000061/pdfft?md5=41dae412c5802b063f8ff0615ba12622\u0026pid=1-s2.0-S2666651021000061-main.pdf) [bib](/bib/Natural-Language-Processing/Knowledge-Graph/Zhang2021Neural.md)\n\n    *Jing Zhang, Bo Chen, Lingxi Zhang, Xirui Ke, Haipeng Ding*\n\n16. **Survey and Open Problems in Privacy Preserving Knowledge Graph: Merging, Query, Representation, Completion and Applications.** arXiv 2020 [paper](https://arxiv.org/pdf/2011.10180.pdf) [bib](/bib/Natural-Language-Processing/Knowledge-Graph/Chen2020Survey.md)\n\n    *Chaochao Chen, Jamie Cui, Guanfeng Liu, Jia Wu, Li Wang*\n\n17. **The Contribution of Knowledge in Visiolinguistic Learning: A Survey on Tasks and Challenges.** arXiv 2023 [paper](https://arxiv.org/abs/2303.02411) [bib](/bib/Natural-Language-Processing/Knowledge-Graph/Lymperaiou2023The.md)\n\n    *Maria Lymperaiou, Giorgos Stamou*\n\n#### [Language Grounding to Vision, Robotics and Beyond](#content)\n\n1. **A comprehensive survey of mostly textual document segmentation algorithms since 2008.** Pattern Recognit. 2017 [paper](https://www.sciencedirect.com/science/article/abs/pii/S0031320316303399) [bib](/bib/Natural-Language-Processing/Language-Grounding-to-Vision,-Robotics-and-Beyond/Eskenazi2017A.md)\n\n    *Sébastien Eskenazi, Petra Gomez-Krämer, Jean-Marc Ogier*\n\n2. **Emotionally-Aware Chatbots: A Survey.** arXiv 2019 [paper](https://arxiv.org/abs/1906.09774) [bib](/bib/Natural-Language-Processing/Language-Grounding-to-Vision,-Robotics-and-Beyond/Pamungkas2019Emotionally-Aware.md)\n\n    *Endang Wahyu Pamungkas*\n\n3. **From Show to Tell: A Survey on Deep Learning-based Image Captioning.** IEEE Trans. Pattern Anal. Mach. Intell. 2023 [paper](https://arxiv.org/pdf/2107.06912.pdf) [bib](/bib/Natural-Language-Processing/Language-Grounding-to-Vision,-Robotics-and-Beyond/Stefanini2023From.md)\n\n    *Matteo Stefanini, Marcella Cornia, Lorenzo Baraldi, Silvia Cascianelli, Giuseppe Fiameni, Rita Cucchiara*\n\n4. **Trends in Integration of Vision and Language Research: A Survey of Tasks, Datasets, and Methods.** J. Artif. Intell. Res. 2021 [paper](https://arxiv.org/abs/1907.09358) [bib](/bib/Natural-Language-Processing/Language-Grounding-to-Vision,-Robotics-and-Beyond/Mogadala2021Trends.md)\n\n    *Aditya Mogadala, Marimuthu Kalimuthu, Dietrich Klakow*\n\n#### [Large Language Models](#content)\n\n1. **A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT.** arXiv 2023 [paper](https://arxiv.org/abs/2303.04226) [bib](/bib/Natural-Language-Processing/Large-Language-Models/Cao2023A.md)\n\n    *Yihan Cao, Siyu Li, Yixin Liu, Zhiling Yan, Yutong Dai, Philip S. Yu, Lichao Sun*\n\n2. **A Comprehensive Survey on Pretrained Foundation Models: A History from BERT to ChatGPT.** arXiv 2023 [paper](https://arxiv.org/abs/2302.09419) [bib](/bib/Natural-Language-Processing/Large-Language-Models/Zhou2023A.md)\n\n    *Ce Zhou, Qian Li, Chen Li, Jun Yu, Yixin Liu, Guangjing Wang, Kai Zhang, Cheng Ji, Qiben Yan, Lifang He, Hao Peng, Jianxin Li, Jia Wu, Ziwei Liu, Pengtao Xie, Caiming Xiong, Jian Pei, Philip S. Yu, Lichao Sun*\n\n3. **A Survey of Safety and Trustworthiness of Large Language Models through the Lens of Verification and Validation.** arXiv 2023 [paper](https://arxiv.org/abs/2305.11391) [bib](/bib/Natural-Language-Processing/Large-Language-Models/Huang2023A.md)\n\n    *Xiaowei Huang, Wenjie Ruan, Wei Huang, Gaojie Jin, Yi Dong, Changshun Wu, Saddek Bensalem, Ronghui Mu, Yi Qi, Xingyu Zhao, Kaiwen Cai, Yanghao Zhang, Sihao Wu, Peipei Xu, Dengyu Wu, Andre Freitas, Mustafa A. Mustafa*\n\n4. **A Survey on In-context Learning.** arXiv 2023 [paper](https://arxiv.org/abs/2301.00234) [bib](/bib/Natural-Language-Processing/Large-Language-Models/Dong2023A.md)\n\n    *Qingxiu Dong, Lei Li, Damai Dai, Ce Zheng, Zhiyong Wu, Baobao Chang, Xu Sun, Jingjing Xu, Lei Li, Zhifang Sui*\n\n5. **A Survey of Large Language Models.** arXiv 2023 [paper](https://arxiv.org/abs/2303.18223) [bib](/bib/Natural-Language-Processing/Large-Language-Models/Zhao2023A.md)\n\n    *Wayne Xin Zhao, Kun Zhou, Junyi Li, Tianyi Tang, Xiaolei Wang, Yupeng Hou, Yingqian Min, Beichen Zhang, Junjie Zhang, Zican Dong, Yifan Du, Chen Yang, Yushuo Chen, Zhipeng Chen, Jinhao Jiang, Ruiyang Ren, Yifan Li, Xinyu Tang, Zikang Liu, Peiyu Liu, Jian-Yun Nie, Ji-Rong Wen*\n\n6. **AI-Augmented Surveys: Leveraging Large Language Models for Opinion Prediction in Nationally Representative Surveys.** arXiv 2023 [paper](https://arxiv.org/abs/2305.09620) [bib](/bib/Natural-Language-Processing/Large-Language-Models/Kim2023AI-Augmented.md)\n\n    *Junsol Kim, Byungkyu Lee*\n\n7. **Bridging the Gap: A Survey on Integrating (Human) Feedback for Natural Language Generation.** arXiv 2023 [paper](https://arxiv.org/abs/2305.00955) [bib](/bib/Natural-Language-Processing/Large-Language-Models/Fernandes2023Bridging.md)\n\n    *Patrick Fernandes, Aman Madaan, Emmy Liu, António Farinhas, Pedro Henrique Martins, Amanda Bertsch, José G. C. de Souza, Shuyan Zhou, Tongshuang Wu, Graham Neubig, André F. T. Martins*\n\n8. **Eight Things to Know about Large Language Models.** arXiv 2023 [paper](https://arxiv.org/abs/2304.00612) [bib](/bib/Natural-Language-Processing/Large-Language-Models/Bowman2023Eight.md)\n\n    *Samuel R. Bowman*\n\n9. **Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyond.** arXiv 2023 [paper](https://arxiv.org/abs/2304.13712) [bib](/bib/Natural-Language-Processing/Large-Language-Models/Yang2023Harnessing.md)\n\n    *Jingfeng Yang, Hongye Jin, Ruixiang Tang, Xiaotian Han, Qizhang Feng, Haoming Jiang, Bing Yin, Xia Hu*\n\n10. **Language Model Behavior: A Comprehensive Survey.** arXiv 2023 [paper](https://arxiv.org/abs/2303.11504) [bib](/bib/Natural-Language-Processing/Large-Language-Models/Chang2023Language.md)\n\n    *Tyler A. Chang, Benjamin K. Bergen*\n\n11. **Large Language Models Meet NL2Code: A Survey.** arXiv 2023 [paper](https://arxiv.org/abs/2212.09420) [bib](/bib/Natural-Language-Processing/Large-Language-Models/Zan2023Large.md)\n\n    *Daoguang Zan, Bei Chen, Fengji Zhang, Dianjie Lu, Bingchao Wu, Bei Guan, Yongji Wang, Jian-Guang Lou*\n\n12. **Large-scale Multi-Modal Pre-trained Models: A Comprehensive Survey.** arXiv 2023 [paper](https://arxiv.org/abs/2302.10035) [bib](/bib/Natural-Language-Processing/Large-Language-Models/Wang2023Large-scale.md)\n\n    *Xiao Wang, Guangyao Chen, Guangwu Qian, Pengcheng Gao, Xiao-Yong Wei, Yaowei Wang, Yonghong Tian, Wen Gao*\n\n13. **On Efficient Training of Large-Scale Deep Learning Models: A Literature Review.** arXiv 2023 [paper](https://arxiv.org/abs/2304.03589) [bib](/bib/Natural-Language-Processing/Large-Language-Models/Shen2023On.md)\n\n    *Li Shen, Yan Sun, Zhiyuan Yu, Liang Ding, Xinmei Tian, Dacheng Tao*\n\n14. **One Small Step for Generative AI, One Giant Leap for AGI: A Complete Survey on ChatGPT in AIGC Era.** arXiv 2023 [paper](https://arxiv.org/abs/2304.06488) [bib](/bib/Natural-Language-Processing/Large-Language-Models/Zhang2023One.md)\n\n    *Chaoning Zhang, Chenshuang Zhang, Chenghao Li, Yu Qiao, Sheng Zheng, Sumit Kumar Dam, Mengchun Zhang, Jung Uk Kim, Seong Tae Kim, Jinwoo Choi, Gyeong-Moon Park, Sung-Ho Bae, Lik-Hang Lee, Pan Hui, In So Kweon, Choong Seon Hong*\n\n15. **Perception, performance, and detectability of conversational artificial intelligence across 32 university courses.** arXiv 2023 [paper](https://arxiv.org/abs/2305.13934) [bib](/bib/Natural-Language-Processing/Large-Language-Models/Ibrahim2023Perception.md)\n\n    *Hazem Ibrahim, Fengyuan Liu, Rohail Asim, Balaraju Battu, Sidahmed Benabderrahmane, Bashar Alhafni, Wifag Adnan, Tuka Alhanai, Bedoor AlShebli, Riyadh Baghdadi, Jocelyn J. Bélanger, Elena Beretta, Kemal Celik, Moumena Chaqfeh, Mohammed F. Daqaq, Zaynab El Bernoussi, Daryl Fougnie, Borja Garcia de Soto, Alberto Gandolfi, Andras Gyorgy, Nizar Habash, J. Andrew Harris, Aaron Kaufman, Lefteris Kirousis, Korhan Kocak*\n\n16. **Recent Advances in Natural Language Processing via Large Pre-Trained Language Models: A Survey.** arXiv 2021 [paper](https://arxiv.org/abs/2111.01243) [bib](/bib/Natural-Language-Processing/Large-Language-Models/Min2021Recent.md)\n\n    *Bonan Min, Hayley Ross, Elior Sulem, Amir Pouran Ben Veyseh, Thien Huu Nguyen, Oscar Sainz, Eneko Agirre, Ilana Heintz, Dan Roth*\n\n17. **Shortcut Learning of Large Language Models in Natural Language Understanding: A Survey.** arXiv 2022 [paper](https://arxiv.org/pdf/2208.11857.pdf) [bib](/bib/Natural-Language-Processing/Large-Language-Models/Du2022Shortcut.md)\n\n    *Mengnan Du, Fengxiang He, Na Zou, Dacheng Tao, Xia Hu*\n\n18. **Summary of ChatGPT/GPT-4 Research and Perspective Towards the Future of Large Language Models.** arXiv 2023 [paper](https://arxiv.org/abs/2304.01852) [bib](/bib/Natural-Language-Processing/Large-Language-Models/Liu2023Summary.md)\n\n    *Yiheng Liu, Tianle Han, Siyuan Ma, Jiayue Zhang, Yuanyuan Yang, Jiaming Tian, Hao He, Antong Li, Mengshen He, Zhengliang Liu, Zihao Wu, Dajiang Zhu, Xiang Li, Ning Qiang, Dingang Shen, Tianming Liu, Bao Ge*\n\n19. **The Contribution of Knowledge in Visiolinguistic Learning: A Survey on Tasks and Challenges.** arXiv 2023 [paper](https://arxiv.org/abs/2303.02411) [bib](/bib/Natural-Language-Processing/Large-Language-Models/Lymperaiou2023The.md)\n\n    *Maria Lymperaiou, Giorgos Stamou*\n\n20. **The Science of Detecting LLM-Generated Texts.** arXiv 2023 [paper](https://arxiv.org/abs/2303.07205) [bib](/bib/Natural-Language-Processing/Large-Language-Models/Tang2023The.md)\n\n    *Ruixiang Tang, Yu-Neng Chuang, Xia Hu*\n\n21. **The Shaky Foundations of Clinical Foundation Models: A Survey of Large Language Models and Foundation Models for EMRs.** arXiv 2023 [paper](https://arxiv.org/abs/2303.12961) [bib](/bib/Natural-Language-Processing/Large-Language-Models/Wornow2023The.md)\n\n    *Michael Wornow, Yizhe Xu, Rahul Thapa, Birju S. Patel, Ethan Steinberg, Scott L. Fleming, Michael A. Pfeffer, Jason A. Fries, Nigam H. Shah*\n\n22. **Towards Reasoning in Large Language Models: A Survey.** arXiv 2022 [paper](https://arxiv.org/pdf/2212.10403.pdf) [bib](/bib/Natural-Language-Processing/Large-Language-Models/Huang2022Towards.md)\n\n    *Jie Huang, Kevin Chen-Chuan Chang*\n\n23. **Tricking LLMs into Disobedience: Understanding, Analyzing, and Preventing Jailbreaks.** arXiv 2023 [paper](https://arxiv.org/abs/2305.14965) [bib](/bib/Natural-Language-Processing/Large-Language-Models/Rao2023Tricking.md)\n\n    *Abhinav Rao, Sachin Vashistha, Atharva Naik, Somak Aditya, Monojit Choudhury*\n\n#### [Linguistic Theories, Cognitive Modeling and Psycholinguistics](#content)\n\n1. **A Survey of Code-switching: Linguistic and Social Perspectives for Language Technologies.** ACL 2021 [paper](https://aclanthology.org/2021.acl-long.131.pdf) [bib](/bib/Natural-Language-Processing/Linguistic-Theories,-Cognitive-Modeling-and-Psycholinguistics/Dogruöz2021A.md)\n\n    *A. Seza Dogruöz, Sunayana Sitaram, Barbara E. 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Hovy*\n\n4. **A Survey of Neural Network Techniques for Feature Extraction from Text.** arXiv 2017 [paper](https://arxiv.org/abs/1704.08531) [bib](/bib/Natural-Language-Processing/Machine-Learning-for-NLP/John2017A.md)\n\n    *Vineet John*\n\n5. **A Survey of Neural Networks and Formal Languages.** arXiv 2020 [paper](https://arxiv.org/abs/2006.01338) [bib](/bib/Natural-Language-Processing/Machine-Learning-for-NLP/Ackerman2020A.md)\n\n    *Joshua Ackerman, George Cybenko*\n\n6. **A Survey of the Usages of Deep Learning in Natural Language Processing.** arXiv 2018 [paper](https://arxiv.org/pdf/1807.10854) [bib](/bib/Natural-Language-Processing/Machine-Learning-for-NLP/Otter2018A.md)\n\n    *Daniel W. Otter, Julian R. Medina, Jugal K. Kalita*\n\n7. **A Survey on Contextual Embeddings.** arXiv 2020 [paper](https://arxiv.org/abs/2003.07278) [bib](/bib/Natural-Language-Processing/Machine-Learning-for-NLP/Liu2020A.md)\n\n    *Qi Liu, Matt J. 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Raina MacIntyre*\n\n14. **The Potential of Machine Learning and NLP for Handling Students' Feedback (A Short Survey).** arXiv 2020 [paper](https://arxiv.org/pdf/2011.05806) [bib](/bib/Natural-Language-Processing/NLP-Applications/Edalati2020The.md)\n\n    *Maryam Edalati*\n\n15. **Towards Improved Model Design for Authorship Identification: A Survey on Writing Style Understanding.** arXiv 2020 [paper](https://arxiv.org/pdf/2009.14445.pdf) [bib](/bib/Natural-Language-Processing/NLP-Applications/Ma2020Towards.md)\n\n    *Weicheng Ma, Ruibo Liu, Lili Wang, Soroush Vosoughi*\n\n#### [Pretrained Models](#content)\n\n1. **A Primer on Contrastive Pretraining in Language Processing: Methods, Lessons Learned and Perspectives.** arXiv 2021 [paper](https://arxiv.org/abs/2102.12982) [bib](/bib/Natural-Language-Processing/Pretrained-Models/Rethmeier2021A.md)\n\n    *Nils Rethmeier, Isabelle Augenstein*\n\n2. **A Review on Language Models as Knowledge Bases.** arXiv 2022 [paper](https://arxiv.org/pdf/2204.06031.pdf) [bib](/bib/Natural-Language-Processing/Pretrained-Models/AlKhamissi2022A.md)\n\n    *Badr AlKhamissi, Millicent Li, Asli Celikyilmaz, Mona T. Diab, Marjan Ghazvininejad*\n\n3. **A Short Survey of Pre-trained Language Models for Conversational AI-A NewAge in NLP.** arXiv 2021 [paper](https://arxiv.org/abs/2104.10810) [bib](/bib/Natural-Language-Processing/Pretrained-Models/Zaib2021A.md)\n\n    *Munazza Zaib, Quan Z. Sheng, Wei Emma Zhang*\n\n4. **A Survey of Controllable Text Generation using Transformer-based Pre-trained Language Models.** arXiv 2022 [paper](https://arxiv.org/pdf/2201.05337.pdf) [bib](/bib/Natural-Language-Processing/Pretrained-Models/Zhang2022A.md)\n\n    *Hanqing Zhang, Haolin Song, Shaoyu Li, Ming Zhou, Dawei Song*\n\n5. **A Survey of Vision-Language Pre-Trained Models.** IJCAI 2022 [paper](https://www.ijcai.org/proceedings/2022/0762.pdf) [bib](/bib/Natural-Language-Processing/Pretrained-Models/Du2022A.md)\n\n    *Yifan Du, Zikang Liu, Junyi Li, Wayne Xin Zhao*\n\n6. **A Survey on Time-Series Pre-Trained Models.** arXiv 2023 [paper](https://arxiv.org/abs/2305.10716) [bib](/bib/Natural-Language-Processing/Pretrained-Models/Ma2023A.md)\n\n    *Qianli Ma, Zhen Liu, Zhenjing Zheng, Ziyang Huang, Siying Zhu, Zhongzhong Yu, James T. Kwok*\n\n7. **AMMUS : A Survey of Transformer-based Pretrained Models in Natural Language Processing.** arXiv 2021 [paper](https://arxiv.org/pdf/2108.05542.pdf) [bib](/bib/Natural-Language-Processing/Pretrained-Models/Kalyan2021AMMUS.md)\n\n    *Katikapalli Subramanyam Kalyan, Ajit Rajasekharan, Sivanesan Sangeetha*\n\n8. **Commonsense Reasoning for Conversational AI: A Survey of the State of the Art.** arXiv 2023 [paper](https://arxiv.org/abs/2302.07926) [bib](/bib/Natural-Language-Processing/Pretrained-Models/Richardson2023Commonsense.md)\n\n    *Christopher Richardson, Larry Heck*\n\n9. **Dense Text Retrieval based on Pretrained Language Models: A Survey.** arXiv 2022 [paper](https://arxiv.org/pdf/2211.14876.pdf) [bib](/bib/Natural-Language-Processing/Pretrained-Models/Zhao2022Dense.md)\n\n    *Wayne Xin Zhao, Jing Liu, Ruiyang Ren, Ji-Rong Wen*\n\n10. **Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processing.** ACM Comput. Surv. 2023 [paper](https://arxiv.org/pdf/2107.13586.pdf) [bib](/bib/Natural-Language-Processing/Pretrained-Models/Liu2023Pre-train.md)\n\n    *Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, Graham Neubig*\n\n11. **Pretrained Language Models for Text Generation: A Survey.** arXiv 2021 [paper](https://arxiv.org/abs/2201.05273) [bib](/bib/Natural-Language-Processing/Pretrained-Models/Li2021Pretrained.md)\n\n    *Junyi Li, Tianyi Tang, Wayne Xin Zhao, Ji-Rong Wen*\n\n12. **Pre-trained models for natural language processing: A survey.** arXiv 2020 [paper](https://link.springer.com/content/pdf/10.1007/s11431-020-1647-3.pdf) [bib](/bib/Natural-Language-Processing/Pretrained-Models/Qiu2020Pre-trained.md)\n\n    *Xipeng Qiu, Tianxiang Sun, Yige Xu, Yunfan Shao, Ning Dai, Xuanjing Huang*\n\n13. **Pre-Trained Models: Past, Present and Future.** arXiv 2021 [paper](https://arxiv.org/abs/2106.07139) [bib](/bib/Natural-Language-Processing/Pretrained-Models/Han2021Pre-Trained.md)\n\n    *Xu Han, Zhengyan Zhang, Ning Ding, Yuxian Gu, Xiao Liu, Yuqi Huo, Jiezhong Qiu, Liang Zhang, Wentao Han, Minlie Huang, Qin Jin, Yanyan Lan, Yang Liu, Zhiyuan Liu, Zhiwu Lu, Xipeng Qiu, Ruihua Song, Jie Tang, Ji-Rong Wen, Jinhui Yuan, Wayne Xin Zhao, Jun Zhu*\n\n14. **Pretrained Transformers for Text Ranking: BERT and Beyond.** WSDM 2021 [paper](https://dl.acm.org/doi/pdf/10.1145/3437963.3441667) [bib](/bib/Natural-Language-Processing/Pretrained-Models/Yates2021Pretrained.md)\n\n    *Andrew Yates, Rodrigo Nogueira, Jimmy Lin*\n\n15. **Pre-training Methods in Information Retrieval.** Found. Trends Inf. Retr. 2022 [paper](https://arxiv.org/pdf/2111.13853.pdf) [bib](/bib/Natural-Language-Processing/Pretrained-Models/Fan2022Pre-training.md)\n\n    *Yixing Fan, Xiaohui Xie, Yinqiong Cai, Jia Chen, Xinyu Ma, Xiangsheng Li, Ruqing Zhang, Jiafeng Guo*\n\n16. **Survey: Transformer based Video-Language Pre-training.** AI Open 2022 [paper](https://arxiv.org/pdf/2109.09920.pdf) [bib](/bib/Natural-Language-Processing/Pretrained-Models/Ruan2022Survey.md)\n\n    *Ludan Ruan, Qin Jin*\n\n17. **VLP: A Survey on Vision-Language Pre-training.** Int. J. Autom. Comput. 2023 [paper](https://arxiv.org/pdf/2202.09061.pdf) [bib](/bib/Natural-Language-Processing/Pretrained-Models/Chen2023VLP.md)\n\n    *Feilong Chen, Duzhen Zhang, Minglun Han, Xiu-Yi Chen, Jing Shi, Shuang Xu, Bo Xu*\n\n#### [Prompt](#content)\n\n1. **Is Prompt All You Need? No. A Comprehensive and Broader View of Instruction Learning.** arXiv 2023 [paper](https://arxiv.org/abs/2303.10475) [bib](/bib/Natural-Language-Processing/Prompt/Lou2023Is.md)\n\n    *Renze Lou, Kai Zhang, Wenpeng Yin*\n\n2. **OpenPrompt: An Open-source Framework for Prompt-learning.** ACL 2022 [paper](https://aclanthology.org/2022.acl-demo.10.pdf) [bib](/bib/Natural-Language-Processing/Prompt/Ding2022OpenPrompt.md)\n\n    *Ning Ding, Shengding Hu, Weilin Zhao, Yulin Chen, Zhiyuan Liu, Haitao Zheng, Maosong Sun*\n\n3. **Reasoning with Language Model Prompting: A Survey.** arXiv 2022 [paper](https://arxiv.org/pdf/2212.09597.pdf) [bib](/bib/Natural-Language-Processing/Prompt/Qiao2022Reasoning.md)\n\n    *Shuofei Qiao, Yixin Ou, Ningyu Zhang, Xiang Chen, Yunzhi Yao, Shumin Deng, Chuanqi Tan, Fei Huang, Huajun Chen*\n\n#### [Question Answering](#content)\n\n1. **A Survey of Question Answering over Knowledge Base.** CCKS 2019 [paper](https://link.springer.com/chapter/10.1007%2F978-981-15-1956-7_8) [bib](/bib/Natural-Language-Processing/Question-Answering/Wu2019A.md)\n\n    *Peiyun Wu, Xiaowang Zhang, Zhiyong Feng*\n\n2. **A Survey on Complex Knowledge Base Question Answering: Methods, Challenges and Solutions.** IJCAI 2021 [paper](https://arxiv.org/abs/2105.11644) [bib](/bib/Natural-Language-Processing/Question-Answering/Lan2021A.md)\n\n    *Yunshi Lan, Gaole He, Jinhao Jiang, Jing Jiang, Wayne Xin Zhao, Ji-Rong Wen*\n\n3. **A Survey on Complex Question Answering over Knowledge Base: Recent Advances and Challenges.** arXiv 2020 [paper](https://arxiv.org/abs/2007.13069) [bib](/bib/Natural-Language-Processing/Question-Answering/Fu2020A.md)\n\n    *Bin Fu, Yunqi Qiu, Chengguang Tang, Yang Li, Haiyang Yu, Jian Sun*\n\n4. **A Survey on Multi-hop Question Answering and Generation.** arXiv 2022 [paper](https://arxiv.org/pdf/2204.09140.pdf) [bib](/bib/Natural-Language-Processing/Question-Answering/Mavi2022A.md)\n\n    *Vaibhav Mavi, Anubhav Jangra, Adam Jatowt*\n\n5. **A survey on question answering technology from an information retrieval perspective.** Inf. Sci. 2011 [paper](https://www.sciencedirect.com/science/article/pii/S0020025511003860) [bib](/bib/Natural-Language-Processing/Question-Answering/Kolomiyets2011A.md)\n\n    *Oleksandr Kolomiyets, Marie-Francine Moens*\n\n6. **A Survey on Why-Type Question Answering Systems.** arXiv 2019 [paper](https://arxiv.org/abs/1911.04879) [bib](/bib/Natural-Language-Processing/Question-Answering/Breja2019A.md)\n\n    *Manvi Breja, Sanjay Kumar Jain*\n\n7. **Complex Knowledge Base Question Answering: A Survey.** arXiv 2021 [paper](https://arxiv.org/pdf/2108.06688.pdf) [bib](/bib/Natural-Language-Processing/Question-Answering/Lan2021Complex.md)\n\n    *Yunshi Lan, Gaole He, Jinhao Jiang, Jing Jiang, Wayne Xin Zhao, Ji-Rong Wen*\n\n8. **Core techniques of question answering systems over knowledge bases: a survey.** Knowl. Inf. Syst. 2018 [paper](https://link.springer.com/article/10.1007/s10115-017-1100-y) [bib](/bib/Natural-Language-Processing/Question-Answering/Diefenbach2018Core.md)\n\n    *Dennis Diefenbach, Vanessa López, Kamal Deep Singh, Pierre Maret*\n\n9. **Introduction to Neural Network based Approaches for Question Answering over Knowledge Graphs.** arXiv 2019 [paper](https://arxiv.org/abs/1907.09361) [bib](/bib/Natural-Language-Processing/Question-Answering/Chakraborty2019Introduction.md)\n\n    *Nilesh Chakraborty, Denis Lukovnikov, Gaurav Maheshwari, Priyansh Trivedi, Jens Lehmann, Asja Fischer*\n\n10. **Narrative Question Answering with Cutting-Edge Open-Domain QA Techniques: A Comprehensive Study.** Trans. Assoc. Comput. Linguistics 2021 [paper](https://arxiv.org/abs/2106.03826) [bib](/bib/Natural-Language-Processing/Question-Answering/Mou2021Narrative.md)\n\n    *Xiangyang Mou, Chenghao Yang, Mo Yu, Bingsheng Yao, Xiaoxiao Guo, Saloni Potdar, Hui Su*\n\n11. **Question Answering Systems: Survey and Trends.** Procedia Computer Science 2015 [paper](https://www.sciencedirect.com/science/article/pii/S1877050915034663) [bib](/bib/Natural-Language-Processing/Question-Answering/Bouziane2015Question.md)\n\n    *Abdelghani Bouziane, Djelloul Bouchiha, Noureddine Doumi, Mimoun Malki*\n\n12. **Retrieving and Reading: A Comprehensive Survey on Open-domain Question Answering.** arXiv 2021 [paper](http://arxiv.org/pdf/2101.00774.pdf) [bib](/bib/Natural-Language-Processing/Question-Answering/Zhu2021Retrieving.md)\n\n    *Fengbin Zhu, Wenqiang Lei, Chao Wang, Jianming Zheng, Soujanya Poria, Tat-Seng Chua*\n\n13. **Survey of Visual Question Answering: Datasets and Techniques.** arXiv 2017 [paper](https://arxiv.org/abs/1705.03865) [bib](/bib/Natural-Language-Processing/Question-Answering/Gupta2017Survey.md)\n\n    *Akshay Kumar Gupta*\n\n14. **Text-based Question Answering from Information Retrieval and Deep Neural Network Perspectives: A Survey.** WIREs Data Mining Knowl. Discov. 2021 [paper](https://arxiv.org/abs/2002.06612) [bib](/bib/Natural-Language-Processing/Question-Answering/Abbasiantaeb2021Text-based.md)\n\n    *Zahra Abbasiantaeb, Saeedeh Momtazi*\n\n15. **Tutorial on Answering Questions about Images with Deep Learning.** arXiv 2016 [paper](https://arxiv.org/abs/1610.01076) [bib](/bib/Natural-Language-Processing/Question-Answering/Malinowski2016Tutorial.md)\n\n    *Mateusz Malinowski, Mario Fritz*\n\n16. **Visual Question Answering using Deep Learning: A Survey and Performance Analysis.** CVIP 2020 [paper](https://arxiv.org/abs/1909.01860) [bib](/bib/Natural-Language-Processing/Question-Answering/Srivastava2020Visual.md)\n\n    *Yash Srivastava, Vaishnav Murali, Shiv Ram Dubey, Snehasis Mukherjee*\n\n#### [Reading Comprehension](#content)\n\n1. **A Survey on Explainability in Machine Reading Comprehension.** arXiv 2020 [paper](http://arxiv.org/pdf/2010.00389.pdf) [bib](/bib/Natural-Language-Processing/Reading-Comprehension/Thayaparan2020A.md)\n\n    *Mokanarangan Thayaparan, Marco Valentino, André Freitas*\n\n2. **A Survey on Machine Reading Comprehension Systems.** Nat. Lang. Eng. 2022 [paper](https://arxiv.org/abs/2001.01582) [bib](/bib/Natural-Language-Processing/Reading-Comprehension/Baradaran2022A.md)\n\n    *Razieh Baradaran, Razieh Ghiasi, Hossein Amirkhani*\n\n3. **A Survey on Machine Reading Comprehension: Tasks, Evaluation Metrics, and Benchmark Datasets.** arXiv 2020 [paper](https://www.mdpi.com/2076-3417/10/21/7640/html) [bib](/bib/Natural-Language-Processing/Reading-Comprehension/Zeng2020A.md)\n\n    *Chengchang Zeng, Shaobo Li, Qin Li, Jie Hu, Jianjun Hu*\n\n4. **A Survey on Neural Machine Reading Comprehension.** arXiv 2019 [paper](https://arxiv.org/abs/1906.03824) [bib](/bib/Natural-Language-Processing/Reading-Comprehension/Qiu2019A.md)\n\n    *Boyu Qiu, Xu Chen, Jungang Xu, Yingfei Sun*\n\n5. **English Machine Reading Comprehension Datasets: A Survey.** EMNLP 2021 [paper](https://arxiv.org/abs/2101.10421) [bib](/bib/Natural-Language-Processing/Reading-Comprehension/Dzendzik2021English.md)\n\n    *Daria Dzendzik, Jennifer Foster, Carl Vogel*\n\n6. **Machine Reading Comprehension: a Literature Review.** arXiv 2019 [paper](https://arxiv.org/abs/1907.01686) [bib](/bib/Natural-Language-Processing/Reading-Comprehension/Zhang2019Machine.md)\n\n    *Xin Zhang, An Yang, Sujian Li, Yizhong Wang*\n\n7. **Machine Reading Comprehension: The Role of Contextualized Language Models and Beyond.** arXiv 2020 [paper](https://arxiv.org/abs/2005.06249) [bib](/bib/Natural-Language-Processing/Reading-Comprehension/Zhang2020Machine.md)\n\n    *Zhuosheng Zhang, Hai Zhao, Rui Wang*\n\n8. **Neural Machine Reading Comprehension: Methods and Trends.** arXiv 2019 [paper](https://arxiv.org/abs/1907.01118) [bib](/bib/Natural-Language-Processing/Reading-Comprehension/Liu2019Neural.md)\n\n    *Shanshan Liu, Xin Zhang, Sheng Zhang, Hui Wang, Weiming Zhang*\n\n#### [Recommender Systems](#content)\n\n1. **A Review of Dataset and Labeling Methods for Causality Extraction.** COLING 2020 [paper](https://aclanthology.org/2020.coling-main.133) [bib](/bib/Natural-Language-Processing/Recommender-Systems/Xu2020A.md)\n\n    *Jinghang Xu, Wanli Zuo, Shining Liang, Xianglin Zuo*\n\n2. **A review on deep learning for recommender systems: challenges and remedies.** Artif. Intell. Rev. 2019 [paper](https://link.springer.com/article/10.1007/s10462-018-9654-y) [bib](/bib/Natural-Language-Processing/Recommender-Systems/Batmaz2019A.md)\n\n    *Zeynep Batmaz, Ali Yürekli, Alper Bilge, Cihan Kaleli*\n\n3. **A Survey of Accuracy Evaluation Metrics of Recommendation Tasks.** J. Mach. Learn. Res. 2009 [paper](https://dl.acm.org/doi/pdf/10.5555/1577069.1755883) [bib](/bib/Natural-Language-Processing/Recommender-Systems/Gunawardana2009A.md)\n\n    *Asela Gunawardana, Guy Shani*\n\n4. **A survey of collaborative filtering based social recommender systems.** Comput. Commun. 2014 [paper](https://www.sciencedirect.com/science/article/abs/pii/S0140366413001722) [bib](/bib/Natural-Language-Processing/Recommender-Systems/Yang2014A.md)\n\n    *Xiwang Yang, Yang Guo, Yong Liu, Harald Steck*\n\n5. **A survey of collaborative filtering techniques.** Adv. Artif. Intell. 2009 [paper](https://downloads.hindawi.com/archive/2009/421425.pdf) [bib](/bib/Natural-Language-Processing/Recommender-Systems/Su2009A.md)\n\n    *Xiaoyuan Su, Taghi M. Khoshgoftaar*\n\n6. **A Survey of Deep Reinforcement Learning in Recommender Systems: A Systematic Review and Future Directions.** arXiv 2021 [paper](https://arxiv.org/pdf/2109.03540.pdf) [bib](/bib/Natural-Language-Processing/Recommender-Systems/Chen2021A.md)\n\n    *Xiaocong Chen, Lina Yao, Julian J. McAuley, Guanglin Zhou, Xianzhi Wang*\n\n7. **A Survey of Explanations in Recommender Systems.** ICDE Workshops 2007 [paper](https://ieeexplore.ieee.org/document/4401070) [bib](/bib/Natural-Language-Processing/Recommender-Systems/Tintarev2007A.md)\n\n    *Nava Tintarev, Judith Masthoff*\n\n8. **A Survey on Accuracy-oriented Neural Recommendation: From Collaborative Filtering to Information-rich Recommendation.** arXiv 2021 [paper](https://arxiv.org/pdf/2104.13030.pdf) [bib](/bib/Natural-Language-Processing/Recommender-Systems/Wu2021A.md)\n\n    *Le Wu, Xiangnan He, Xiang Wang, Kun Zhang, Meng Wang*\n\n9. **A survey on Adversarial Recommender Systems: from Attack/Defense strategies to Generative Adversarial Networks.** ACM Comput. Surv. 2022 [paper](https://arxiv.org/abs/2005.10322) [bib](/bib/Natural-Language-Processing/Recommender-Systems/Deldjoo2022A.md)\n\n    *Yashar Deldjoo, Tommaso Di Noia, Felice Antonio Merra*\n\n10. **A Survey on Conversational Recommender Systems.** ACM Comput. Surv. 2022 [paper](https://arxiv.org/abs/2004.00646) [bib](/bib/Natural-Language-Processing/Recommender-Systems/Jannach2022A.md)\n\n    *Dietmar Jannach, Ahtsham Manzoor, Wanling Cai, Li Chen*\n\n11. **A survey on group recommender systems.** J. Intell. Inf. Syst. 2020 [paper](https://link.springer.com/article/10.1007/s10844-018-0542-3) [bib](/bib/Natural-Language-Processing/Recommender-Systems/Dara2020A.md)\n\n    *Sriharsha Dara, C. Ravindranath Chowdary, Chintoo Kumar*\n\n12. **A Survey on Knowledge Graph-Based Recommender Systems.** IEEE Trans. Knowl. Data Eng. 2022 [paper](https://arxiv.org/abs/2003.00911) [bib](/bib/Natural-Language-Processing/Recommender-Systems/Guo2022A.md)\n\n    *Qingyu Guo, Fuzhen Zhuang, Chuan Qin, Hengshu Zhu, Xing Xie, Hui Xiong, Qing He*\n\n13. **A Survey on Personality-Aware Recommendation Systems.** Artif. Intell. Rev. 2022 [paper](http://arxiv.org/pdf/2101.12153.pdf) [bib](/bib/Natural-Language-Processing/Recommender-Systems/Dhelim2022A.md)\n\n    *Sahraoui Dhelim, Nyothiri Aung, Mohammed Amine Bouras, Huansheng Ning, Erik Cambria*\n\n14. **A Survey on Reinforcement Learning for Recommender Systems.** arXiv 2021 [paper](https://arxiv.org/pdf/2109.10665.pdf) [bib](/bib/Natural-Language-Processing/Recommender-Systems/Lin2021A.md)\n\n    *Yuanguo Lin, Yong Liu, Fan Lin, Pengcheng Wu, Wenhua Zeng, Chunyan Miao*\n\n15. **A Survey on Session-based Recommender Systems.** ACM Comput. Surv. 2022 [paper](https://arxiv.org/pdf/1902.04864.pdf) [bib](/bib/Natural-Language-Processing/Recommender-Systems/Wang2022A.md)\n\n    *Shoujin Wang, Longbing Cao, Yan Wang, Quan Z. Sheng, Mehmet A. Orgun, Defu Lian*\n\n16. **A Survey on the Fairness of Recommender Systems.** arXiv 2022 [paper](https://arxiv.org/pdf/2206.03761.pdf) [bib](/bib/Natural-Language-Processing/Recommender-Systems/Wang2022A1.md)\n\n    *Yifan Wang, Weizhi Ma, Min Zhang, Yiqun Liu, Shaoping Ma*\n\n17. **Advances and Challenges in Conversational Recommender Systems: A Survey.** AI Open 2021 [paper](https://arxiv.org/abs/2101.09459) [bib](/bib/Natural-Language-Processing/Recommender-Systems/Gao2021Advances.md)\n\n    *Chongming Gao, Wenqiang Lei, Xiangnan He","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FNiuTrans%2FABigSurvey","html_url":"https://awesome.ecosyste.ms/projects/github.com%2FNiuTrans%2FABigSurvey","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2FNiuTrans%2FABigSurvey/lists"}